Self-sensing and separated dual-cylinder magnetorheological damper

    公开(公告)号:US12228189B2

    公开(公告)日:2025-02-18

    申请号:US17824053

    申请日:2022-05-25

    Abstract: A self-sensing and separated dual-cylinder magnetorheological damper includes a first piston cylinder and a second piston cylinder which are in angular communication with each other. The first piston cylinder includes a piston inner cylinder and a piston outer cylinder which together with the second piston cylinder form a magnetorheological fluid circulation channel. The piston inner cylinder is provided with a piston rod assembly reciprocating in an axial direction of the piston inner cylinder, and when the piston rod assembly is compressed and restored, the magnetorheological liquid correspondingly forms a first circulation loop and a second circulation loop respectively. The second piston cylinder is provided therein with a magnetorheological liquid adjustment mechanism for forming the first circulation loop and the second circulation loop. Independent control of damping force values in compression and restoration working conditions can be achieved by means of different circulation channels of the magnetorheological liquid.

    A method and system for predicting human fall risk based on electronic nursing text data

    公开(公告)号:US20250053739A1

    公开(公告)日:2025-02-13

    申请号:US18272584

    申请日:2022-10-24

    Abstract: The present invention includes techniques for data processing and particularly relates to a method and system for predicting human fall risk based on electronic nursing text (ENT) data. The method comprises the following steps: obtaining an ENT data set, pre-processing data in the ENT data set, and constructing a Morse Fall Scale (MFS) dictionary with the pre-processed data; extracting features from the ENT data of patients to be predicted with a natural language processing technology; analyzing the extracted text features by the MFS dictionary to obtain a data set of risk factors; training a decision tree algorithm by using the data set of risk factors to obtain a prediction result of the patient's fall risk; clustering and precisely nursing the patients according to the prediction result. The invention constructs the MFS dictionary through the electronic health records to obtain fall risk factors of the patients, and iteratively predicts their fall risks according to the risk factors, thereby improving the prediction efficiency.

    Negative stiffness generating mechanism and quasi-zero stiffness vibration isolator

    公开(公告)号:US12203526B2

    公开(公告)日:2025-01-21

    申请号:US17655294

    申请日:2022-03-17

    Abstract: A negative stiffness generating mechanism and a quasi-zero stiffness vibration isolator are provided. A housing is mounted on a base, and the axial relative positions of the housing and the base can be adjusted; a negative stiffness unit comprises inner-ring magnets, outer-ring magnets and a supporting shaft, the supporting shaft axially slides on the base and passes through the housing, the inner-ring magnets fixedly sleeve the supporting shaft, and the outer-ring magnets sleeve outside the inner-ring magnets and are divided into upper and lower groups of outer-ring magnets; the upper and lower groups of outer-ring magnets can synchronously move through a negative stiffness adjusting device; and the axial relative positions of the middle planes of the outer-ring and inner-ring magnets can be adjusted by adjusting the axial relative positions of the housing and the base. The isolator comprises a negative stiffness generating mechanism and a positive stiffness unit.

    CONTRASTIVE LOSS BASED TRAINING STRATEGY FOR UNSUPERVISED MULTI-OBJECT TRACKING

    公开(公告)号:US20240404077A1

    公开(公告)日:2024-12-05

    申请号:US18677886

    申请日:2024-05-30

    Abstract: The present invention relates to unsupervised tracking technology, specifically an unsupervised tracking model training strategy based on contrastive loss. The method comprises: S1: forming a constrained SSCI module using the relation between objects within a video frame and between adjacent video frames; S2: setting features of different objects in each frame as negative samples, and similar adjacent frame objects as positive sample pairs, constructing contrastive loss; S3: constraining embedded features (E_t) by variable loss based on self-supervised contrastive loss. This invention provides a contrastive loss-based training strategy for unsupervised multi-object tracking, leveraging the prior that objects in a frame must be different to enhance object similarity, and using self-supervised learning to match similar objects in short-interval frames as positive samples to boost cross-frame feature expression. Finally, it further improves cross-frame feature expression by ensuring consistent forward and reverse matching.

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